Automatic segmentation of the prostate on CT images using deep neural networks (DNN)
Recommended Citation
Liu C, Gardner SJ, Wen N, Elshaikh MA, Siddiqui F, Movsas B, and Chetty IJ. Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol Biol Phys 2019; Epub ahead of print.
Document Type
Article
Publication Date
3-16-2019
Publication Title
International journal of radiation oncology, biology, physics
Abstract
PURPOSE: Recent advances in deep neural networks (DNN) have unlocked opportunities toward its application for automatic image segmentation. We have evaluated a DNN-based algorithm for automatic segmentation of the prostate gland on a large cohort of patient images.
MATERIALS AND METHODS: Planning-CT (pCT) datasets for 1114 prostate cancer patients were retrospectively selected and divided into 2 groups. Group A contained 1104 datasets, with 1 physician-generated prostate gland contour for each dataset. Among these image sets, 771/193/140 were used for training, validation and testing respectively. Group B contained 10 datasets; each including prostate contours delineated by 5 independent physicians, and a consensus contour generated using the STAPLE method in CERR. All images were resampled to spatial resolution of 1x1x1.5 mm. A region (128x128x64 voxels) containing the prostate was selected to train a DNN. The best-performing model on the validation datasets was used to segment the prostate on all testing images. Results were compared between DNN and physician-generated contours using the Dice coefficient (DSC), Hausdorff distances, regional contour distances, and center-of-mass (COM) distances.
RESULTS: Mean DSC between DNN-based prostate segmentation and physician-generated contours for test data in group A, group B, and group B-consensus was 0.85±0.06 [range=0.65, 0.93], 0.85±0.04 [range=0.80, 0.91], and 0.88±0.03 [range=0.82, 0.92] respectively. The Hausdorff distance was 7.0±3.5 mm, 7.3±2.0 mm, and 6.3±2.0 mm for group A, group B, and group B-consensus respectively. The mean COM distances for all 3 dataset groups were within 5 mm.
CONCLUSIONS: A deep-neural-network-based algorithm was used to automatically segment the prostate for a large cohort of prostate cancer patients. DNN-based prostate segmentations were compared to the consensus contour for a smaller group of patients; the agreement between DNN segmentations and consensus contour was similar to the agreement reported in a previous study. Clinical use of DNN is promising, but further investigation is warranted.
PubMed ID
30890447
ePublication
ePub ahead of print
